When a bank customer attempts to make a check card purchase, withdraw funds from an ATM or make a teller withdrawal, his/her bank must determine whether the customer has a sufficient balances to cover the item, and, if not, whether to authorize the purchase or withdrawal into an overdrawn position or decline the transaction. Similarly, when one or more bank customer check s (or other returnable items) are processed in the nightly posting batch run, the bank must determine whether to pay or return each item that, if posted, would overdraw the customer's account. Each day any given bank may make millions of such authorization/decline and pay/return decisions. Each day the banking community as a whole makes trillions of such decisions. From a customer service perspective, banks would prefer to authorize and pay such transactions. Declines and returns are embarrassing to customers. Declines often lead to less frequent use of a given check card. Returns can lead to additional vendor assessed fees for bounced checks and/or late payment. So customers and banks alike regard it as better customer service when the bank covers an overdraft transaction, but some fraction of the overdrafts thus generated are never repaid. Indeed, at a typical bank, between 4% to 5% of those accounts allowed to overdraw will never fully repay leaving the bank to charge off the negative balance and absorb the loss.
If a bank knew in advance that a given account was headed for charge-off, the bank could forgo decisions that would take that account's balance into a negative position, and authorize and pay only those items where there was no probability of charge-off. Such precise foreknowledge is, of course, impossible. But it is possible to ascertain the probability that a given account will charge off and, based on that probability, a bank can make better decisions about when to authorize and/or pay transactions into overdraft. While there are a variety of software systems and methodologies on the market that address the “overdraft” problem, none function to ascertain either the probability of charge-off or the probability of cure (i.e. the probability that an account with a negative balance will pay back what is owed returning to a positive balance).
Current software systems and methodologies are based on either fixed or user-definable scoring systems. The score is used to control overdrafts through a single quantity called the overdraft limit. The overdraft limit for an account is the maximum negative balance into which a bank will either authorize or pay a transaction.
Overdraft limits are generated by evaluating certain quantities of interest, which, though similar among existing approaches, may vary from one approach to the next. Often, these quantities represent values already available in the DDA (Demand Deposit Account) system, which is the system of record for the balances of each account and for other information about the account and how it is to be processed. Based on the value of one of these quantities, the overdraft limit for the associated account may be either incremented or decremented according to the scoring rules for quantity.
Few of these software systems and methodologies offer objective justification for the inclusion of a particular quantity; none show how these quantities relate to charge-off and cure behavior. In fact, demonstrating such a relationship is not easy because of the time spans involved. At the time when an authorization or pay decision is made, it may require 45 days or more (depending on the charge-off process of the bank) before it is clear whether or not an account charged off and, if so, how much money was lost. Since some fraction of charge-offs are eventually recovered, post charge-off history needs to be studied to determine net losses. Similarly, the decision itself relies on characteristics of the account gathered over some suitable period of time leading up to the decision. (See FIG. 1.) Thus, an objective analysis of the predictive power of any given characteristic requires data collected over an extended period of time. Without such data, one may appeal to intuition, but one cannot bring the power of data mining to bear to assess where one's intuition is correct.
As compared to other situations where banks put principal at risk in order to service customers and assess fees or interest, the overdraft space is very profitable. (Banks do not call overdrafts loans, because they are not committed to extending the overdraft and to make such a commitment would subject the extension to lending regulations which, in many ways, are more stringent than those governing overdrafts.) Overdraft revenue has been made increasingly profitable over the last two decades through a series of marketing, pricing and processing reforms that have significantly enhanced overdraft revenue.
One side effect of these reforms is that overdraft charge-offs have risen disproportionately with revenue. Since the revenues still far outweigh charge-offs, overdrafts are still a very profitable business, but it is clear that the current software systems and methodologies employed to control losses through the setting of overdraft limits are deficient. For this reason a need exists for better, more robust and responsive approaches to address the overdraft problem.